用表格型基础模型精准生成岩体断裂分布，提升小样本下的预测可靠性。
Statistically Accurate and Robust Generative Prediction of Rock Discontinuities with A Tabular Foundation Model
- 基于表格基础模型学习少量实测数据中的复杂分布模式。
- 在10个不同规模数据集上表现优于传统统计与深度生成模型。
- 适合地质建模、工程安全评估等需要高可靠性的场景。
岩体断裂对岩石力学性能和稳定性具有决定性影响，其内部分布难以直接观测，通常需通过地表断裂信息推断。然而地表观测数据稀疏，现有生成预测方法要么无法捕捉复杂的分布规律，要么在数据稀缺时缺乏鲁棒性。本文提出一种简单而稳健的表格型基础模型方法，利用专为小样本设计的模型学习能力，有效提取有限实测数据中的复杂分布特征。在涵盖十组不同尺度与分布模式的岩体断裂数据集上进行对比实验，结果表明该方法在统计准确性与鲁棒性方面均显著优于传统统计模型和深度生成模型。本工作推动了岩体结构的量化表征，有助于实现更安全可靠的地质工程数据驱动设计。
原文摘要 · Abstract (English)
Rock discontinuities critically govern the mechanical behavior and stability of rock masses. Their internal distributions remain largely unobservable and are typically inferred from surface-exposed discontinuities using generative prediction approaches. However, surface-exposed observations are inherently sparse, and existing generative prediction approaches either fail to capture the underlying complex distribution patterns or lack robustness under data-sparse conditions. Here, we proposed a simple yet robust approach for statistically accurate generative prediction of rock discontinuities by utilizing a tabular foundation model. By leveraging the powerful sample learning capability of the foundation model specifically designed for small data, our approach can effectively capture the underlying complex distribution patterns within limited measured discontinuities. Comparative experiments on ten datasets with diverse scales and distribution patterns of discontinuities demonstrate superior accuracy and robustness over conventional statistical models and deep generative approaches. This work advances quantitative characterization of rock mass structures, supporting safer and more reliable data-driven geotechnical design.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。